{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "deca602c",
   "metadata": {},
   "source": [
    "### 导入相关需要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54eb5b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "from core.data_processor import DataLoader\n",
    "import pandas as pd\n",
    "import json\n",
    "from core.model import Model\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68805a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import set_printoptions\n",
    "set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e2cb6b9",
   "metadata": {},
   "source": [
    "#### 画图（可视化函数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df71a2d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(predicted_data, true_data):\n",
    "    fig = plt.figure(facecolor='white')\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(true_data, label='True Data')\n",
    "    plt.plot(predicted_data, label='Prediction')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_results_multiple(predicted_data, true_data, prediction_len):\n",
    "    fig = plt.figure(facecolor='white')\n",
    "    ax = fig.add_subplot(111)\n",
    "    ax.plot(true_data)\n",
    "    # 填充预测列表，将其在图表中移动到正确的开始位置\n",
    "    for i, data in enumerate(predicted_data):\n",
    "        padding = [None for _ in range(i * prediction_len)]\n",
    "        plt.plot(padding + data)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5396c0bd",
   "metadata": {},
   "source": [
    "#### * 1.导入设置文件和数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b664d08",
   "metadata": {},
   "outputs": [],
   "source": [
    "configs = json.load(open('config.json', 'r'))\n",
    "df = pd.read_csv('./data/sh600031.csv')  # 读取股票文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37c8d106",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-07-03</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>20.10</td>\n",
       "      <td>21.30</td>\n",
       "      <td>33816715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-07-04</td>\n",
       "      <td>21.30</td>\n",
       "      <td>22.18</td>\n",
       "      <td>21.05</td>\n",
       "      <td>21.84</td>\n",
       "      <td>7697544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-07-07</td>\n",
       "      <td>21.90</td>\n",
       "      <td>21.96</td>\n",
       "      <td>21.51</td>\n",
       "      <td>21.80</td>\n",
       "      <td>3951205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-07-08</td>\n",
       "      <td>21.80</td>\n",
       "      <td>22.22</td>\n",
       "      <td>21.70</td>\n",
       "      <td>21.91</td>\n",
       "      <td>2117357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-07-09</td>\n",
       "      <td>21.75</td>\n",
       "      <td>22.65</td>\n",
       "      <td>21.70</td>\n",
       "      <td>22.47</td>\n",
       "      <td>3440447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close    volume\n",
       "0  2003-07-03  23.00  23.00  20.10  21.30  33816715\n",
       "1  2003-07-04  21.30  22.18  21.05  21.84   7697544\n",
       "2  2003-07-07  21.90  21.96  21.51  21.80   3951205\n",
       "3  2003-07-08  21.80  22.22  21.70  21.91   2117357\n",
       "4  2003-07-09  21.75  22.65  21.70  22.47   3440447"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "700acb37",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4424, 6)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b6d10e",
   "metadata": {},
   "source": [
    "#### * 2.用DataLoader加载数据df，它将会安装预先设置好的‘train_test_split’和‘columns’对数据进行分割和采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c675830a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = DataLoader(\n",
    "        dataframe=df,\n",
    "        split=configs['data']['train_test_split'],\n",
    "        cols=configs['data']['columns']\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4978e181",
   "metadata": {},
   "source": [
    "     * 2.1 加载训练集，数据采用滑动窗口的方式采样：\n",
    "     按照‘sequence_length’序列长度对数据进行滑动窗口的方式采样：即第n+1个点数据+前n个点数据=sequence_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "90feb6b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = data.get_train_data(\n",
    "        seq_len=configs['data']['sequence_length'],\n",
    "        normalise=configs['data']['normalise']\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a6cb59",
   "metadata": {},
   "source": [
    "    * 2.2 加载训练集，数据采用滑动窗口的方式采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed2a03d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test, y_test = data.get_test_data(\n",
    "        seq_len=configs['data']['sequence_length'],\n",
    "        normalise=configs['data']['normalise']\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3bc24024",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3745, 14, 2), (3745, 1))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ee8b472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((649, 14, 2), (649, 1))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad225894",
   "metadata": {},
   "source": [
    "#### * 3.构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3f3f7c1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Model] Model Compiled\n",
      "Time taken: 0:00:01.452305\n"
     ]
    }
   ],
   "source": [
    "model = Model()\n",
    "model.build_model(configs, x.shape[1], x.shape[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959f2a00",
   "metadata": {},
   "source": [
    "#### * 4.进行训练 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32d67b83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Model] Training Started\n",
      "[Model] 32 epochs, 64 batch size\n",
      "Epoch 1/32\n",
      "59/59 [==============================] - 17s 153ms/step - loss: 0.0096 - val_loss: 0.0030\n",
      "Epoch 2/32\n",
      "59/59 [==============================] - 7s 119ms/step - loss: 0.0055 - val_loss: 0.0022\n",
      "Epoch 3/32\n",
      "59/59 [==============================] - 7s 112ms/step - loss: 0.0042 - val_loss: 0.0017\n",
      "Epoch 4/32\n",
      "59/59 [==============================] - 7s 116ms/step - loss: 0.0036 - val_loss: 0.0015\n",
      "Epoch 5/32\n",
      "59/59 [==============================] - 7s 122ms/step - loss: 0.0031 - val_loss: 0.0012\n",
      "Epoch 6/32\n",
      "59/59 [==============================] - 8s 128ms/step - loss: 0.0027 - val_loss: 0.0016\n",
      "Epoch 7/32\n",
      "59/59 [==============================] - 7s 118ms/step - loss: 0.0025 - val_loss: 0.0011\n",
      "Epoch 8/32\n",
      "59/59 [==============================] - 7s 118ms/step - loss: 0.0023 - val_loss: 9.3241e-04\n",
      "Epoch 9/32\n",
      "59/59 [==============================] - 7s 123ms/step - loss: 0.0021 - val_loss: 8.7964e-04\n",
      "Epoch 10/32\n",
      "59/59 [==============================] - 7s 118ms/step - loss: 0.0019 - val_loss: 8.4625e-04\n",
      "Epoch 11/32\n",
      "59/59 [==============================] - 7s 118ms/step - loss: 0.0020 - val_loss: 9.0441e-04\n",
      "Epoch 12/32\n",
      "59/59 [==============================] - 7s 116ms/step - loss: 0.0019 - val_loss: 8.1439e-04\n",
      "Epoch 13/32\n",
      "59/59 [==============================] - 7s 115ms/step - loss: 0.0018 - val_loss: 7.9197e-04\n",
      "Epoch 14/32\n",
      "59/59 [==============================] - 8s 130ms/step - loss: 0.0018 - val_loss: 7.8074e-04\n",
      "Epoch 15/32\n",
      "59/59 [==============================] - 8s 133ms/step - loss: 0.0017 - val_loss: 7.6958e-04\n",
      "Epoch 16/32\n",
      "59/59 [==============================] - 7s 110ms/step - loss: 0.0017 - val_loss: 8.4075e-04\n",
      "Epoch 17/32\n",
      "59/59 [==============================] - 8s 128ms/step - loss: 0.0018 - val_loss: 7.4587e-04\n",
      "Epoch 18/32\n",
      "59/59 [==============================] - 7s 116ms/step - loss: 0.0016 - val_loss: 7.7211e-04\n",
      "Epoch 19/32\n",
      "59/59 [==============================] - 7s 113ms/step - loss: 0.0016 - val_loss: 7.8124e-04\n",
      "[Model] Training Completed. Model saved as saved_models\n",
      "Time taken: 0:02:24.581219\n"
     ]
    }
   ],
   "source": [
    "# 内存中训练\n",
    "model.train(\n",
    "    x,\n",
    "    y,\n",
    "    epochs=configs['training']['epochs'],\n",
    "    batch_size=configs['training']['batch_size'],\n",
    "    validation_data=(x_test, y_test),\n",
    "    verbose=configs['training']['verbose'],\n",
    "    shuffle=configs['training']['shuffle'],\n",
    "    validation_freq=configs['training']['validation_freq'],\n",
    "    save_dir=configs['model']['save_dir']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "27fc3ec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Model] Predicting Sequences Multiple...\n",
      "[Model] Predicting Sequences Full...\n",
      "[Model] Predicting Point-by-Point...\n"
     ]
    }
   ],
   "source": [
    "# 多序列预测\n",
    "predictions_multiseq = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'],\n",
    "                                                        configs['data']['sequence_length'])\n",
    "\n",
    "# 全系列预测\n",
    "predictions_fullseq = model.predict_sequence_full(x_test, configs['data']['sequence_length'])\n",
    "\n",
    "# 逐点预测\n",
    "predictions_pointbypoint = model.predict_point_by_point(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b856fdd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g6mI4ofaenJuXgaevmQATz3pF6USzhu/y0fAJQ1NsGJJiw9pdpQFe2begBr8HeOaTvh3W9retx7B6xyms3xNanRViLGVZhizLQXu+ZGIx8RyMHIvq9mQwkGC3nyuvSwSgjL0rG6u1LUEafEkGC2ACOBw/1HG9ISKLOQQRLs3KQLsP0O4Ug47Lr9M5zsCxXiuGZo2GH21F1VyiBCPP+mn4tS1O/GB8Jr4trvWrqNkXiUqD7xQkPPDePpS5N6R6G3//LPRqiL0J3h1u02QPrUMQic4RJRn//KYEI3+3xa+GiR7EyBo4BgzDoMGhVFJtb+9+r+tkw0nYhj0Nzqp8Z13ZWA3WMxZECQyAv8AK4buqjo91fyZOQfJaGWg3WX+4cjsmPv5JUNduavcfo9HHw9e2mdxb1hDUefsKiqTjr+GfbXZgwfhMSDKw5eCZHhpd5IhKg/9NUQ3e+74Mv1t/sKeH4oXZEB0fd4xJieZtcXRN0hFl4H/7FO/8VG3nERC+6f8VLUkAgDN13R+Lnx2bDTAuGJO3Aeiahx8oNr62xeFV0tglyhABfA8BqTUdT4TaYm7aMQmijG9P1qK2xYETZ1s6HVu8Rekv8JPpg/z+ZuS9o3S03v7Ok7Wdnrsv4RSVrl+cRsOPNfH4rqQOI9JikJNsxdbDHU/CfYHosEA+hBP+150QQ9nXIe8jVE2XyBCSJKuxzcH0ThV8YsUbHXFwiTwOnCrs6GWRQeYhNE0AZy0GIHRx01b/PT668RAWPL8dR840AfBs2u6DiDiHBLE18AqKGF9Bkr1lHJeIa1ftxDUv7whqbBzL4CfTByHWbPD7m4Fj4BI0kUManT/aauwQSUer4V88MhVHzjTj2NkWzB6djm+Kav06r/U1otrghxr+190YNGnrfXnj1mJUjHWoBl/UROl4DH7nqwTyOhJBIYPFmbY0SK7ijl4WEdqcAkT7QDCsANZc1UUPX/89Hq5UDP3uUw0APBuupXAb89rAHae0E492TGX1ymuKqj0rp47uNbtLhInndL8HI8/BIWpLK+hfPxoQdCSdK8ZmAFA2xmePSoNTkLD9eE1PDTEiRKXBJ9nhva3CaXqcWf1/X66rTgxIq7Orko4cUstA4vlymoextDkLNvZkRMoRdER9mwtie7ZyfXNZlwxdIG+YdM8i74946mVugy/W2QOeUxvCqf0MXvrSX+bqaBXlECRsPliJu/+92+9vRo7xyrTVOlDRVmuGFE/Tevg2o7KSFSQZ+TlJiDXx+KwL3dp6E1Fp8MkD0Ns8fG1t8b5cV5087F318GU5NEnH4+ErD+O7d56PBlcOYgwNePi9L0IaQ6hsO1YN2ZUEWbCCNZd1qYhaoMmdRNCQCYG8z0pIkAAINYE9fO1Grfb/HON/XwWSIVyiBFGSEW824GB5k9/ffTV8UbN53pNROi5RwmMbD6GqKfCEGCqktIJWwycrclGSYeRZXDwiFZ8dOdunI5Si0uCTB6i3GXyXj9baVyEGJtRUe1ENJZTwrXvT76H/7MfB8o57sQo+m7ZThySBMYwEABwp9fdMI0lhRRMABqI9C5ylax6+Q/NdE2MhSbK6QiJSjotMiByDBg4Qajvy8LWx9xIAGb+d9jSuHfm++vuR6bEAvKNrtBCnIyvBgjNNdr+wQyPH+lXLJL/vSQ//y6PV+Oc3JfjD/w5F7JwuUYKRY2EzeurmkPuNTPKzRqXhbLMDhyr8J8e+QlQafIdq8Ht4ID5oDX4wnm1vhcR9h1o9kRi7/WWNqGhUjJkoydi4r+N4el8NHwBONQ0AAAyO697QTOLhivZssKYqCHLosdhekS7uz65F89mpG7Duf2NMPKr5jjV87b10oLwRAINGRywmph7AjdMH4fDjl+PuWbkAAjsX5Q3K+QcmWwHAz5D5xuFLsgyGUcJye1LDJ/cdp1MOoqu4BBk8xyA11tNvm+j5ZFE3c1QaGAb49EjfjdaJcoPfuyy+1lvq05KO6JF0thZW4bpVO4PahA4kt3f2Wl8PHwBq20043ZSF8SmR8/L0IMZaas8Gw0hgTJUhGzuHZnJXwyk1vyNGlXj6MWYeZ1i5Qw9fO4aXvzwJANhfPRbJlnoMiq9RCs25vdV2HQ+/psWBy//2FQAgJ8kGAH4rLd84fFGSwTGKzt2TLQDJs2OJYJgzybTVlof29fCTbEacNyixT4dnRqnBV26I3qa1uUQZcWZlI6hPG3z359riEHDHWwXYcbI2KIkqUFnkzkL8RJ++qwCw4urxKKiaiOGJxWhsCS3jNxRUfd0+EADAmUtDNnZaDZ8Yd63X71RDLJV/bUYeFYwEqdUFKYBsRiaHQUlWHK1qBgCcalI2l2M4pbAciabSk3S05R4SbAYMTrZ65QQA/hq+JCsRcBzL9KiHTyawSJYtJgZfi16HtsvzMnCwvAlF1Z3nOPRGImLwt2zZgpEjRyI3NxcrVqzw+/u2bdtw3nnnged5rFu3LhKX7BDykO4ra8R/vi/r9usFi1OQ1Hjnvq3hK59vY7tLLRfcHISerzcBp8SYOo1YIl6x1sOfOiQJF477MQCgrOJ/QY27K5B7SRbiILniuqTja8MyiaHWTnIk1p28zxgTj1KZhGbqe/lkDLFmT25HRWsGJJmBBSUAAKs7yqTd5f/daGv15OckYeyAeL+NW18NX5JlsKzyPfRkC8B29+rIElGDL6tBFd88PAsf33exxsP3vNeFEweAZRByWZHeQtgGXxRFLF++HJs3b0ZhYSHWrFmDwkLvhJhBgwbhn//8J66//vpwLxcUWgPyy/f2nZNrBoNTlBDnzmx09HINv6bFEbCAF3kAtIW6gjH4eh6+b4Eu3df5dGUixNiG4Xj9UFRX/Ruy3D0TqFbDFtuGgLMdh0MIbe/CqePha8/rEiVUNrbj3nf2AgDS4kw45X4/gXR8shqwajYZXZIRZ9tSYUKx19/0PHyy4b7up+cjK8GCMQPicLquDY1tnmQv4uHLmpIYXK/w8JWxmyLu4Sv314AEC0akx2o0fM97TY8z48LcFLy/p7zXKQjBELbB37VrF3JzczF06FAYjUYsWbIEGzZs8DomJycH48ePBxvBTZaOOJfGdPE/vsHv1h8I6liXKKmSTige/gufn8B3JXXqz6IkY/2e8m556OwuEU9uOYKZf/kCY3//kW6cu5531xxEXR298ZoMrFdyjx6+jbYJ8RYDPj41E6KrHNXVWzu9fldwChIGJlkAACNjzwfLt2Lv2b0hnUPrgOw53aCel+ASJbz6lSeJLCPOglOCCDCAq0q/HhSZbIkXTyhryQQvKZo+8YB1Db57Mo9x349js+IBAIcqPbKOgWMhy94JcyxLNPyeM3ZN7slKjNA+giTJaotDLbyOhw8AV03KQll9u9cz2VcI2wKXl5dj4MCB6s/Z2dkoL+/acmfVqlXIz89Hfn4+qqs7rhYYiDangA17z91yq+BUPf61M7hmHC5BQnKMEUBwBpLwl4+O4scveVLl3/72FO59Zy/e+S7yESo7T9biH18UodltEE5W+9e6UROhNAY4mFrpegbfVzbo6HW+URkJFgP2nB0HmRuA06WvdXr9ruAUJQxLjUHJigX44chZkCUen5eFNrloDf7yf+/G3tIGr985RQlxmtIGNhOHBkFEGSuj8qh+zRpRx8MHgPLmAWCkCohim6rh6+0XEQ+flMnIGxAHADikkXWIxKFuXMsyOLbnPXxyr0WqvAOJnPI1+IFKtFw+NgMxJh7vFvQeuThYetWm7bJly1BQUICCggKkpqZ26RztThEnA7Qk27S/EqV1kaugGWpDCJcoIy1WybatDbJGuh4knK6hPfL14H0f43qdsrkuUUZKjAkDEszgZWCgiw1O0tHz8IOQdMgqQ8/Dl8Gi3fAjNDZ+j8amyMt3TsGzmWfjbRCax+CT0x+izRX8feRbWqGqye7j4cuIt3g8deK1HxRdcJW36EYxBfbwB4CBjJbW416Sju85yIQea1ImmpQYEwbEm7GntF49xuh+39rEMCVKp2fDMslk2dXa/76QFavBRzIkYcC+Hr7VyOO6qQMRY+p7vW7DNvhZWVkoLfV4mmVlZcjKygr3tF0mOcYTR/vLOSMAKDesLMtY/u/duOipz7G1MDJhVZUNHn21M+Mvy7Kq4VsMHOqDborhMQxfn6hBs92lPoBGLvLztcPHG2xo81+JCG6902bkMcPOY3GrEQ01nRtAvTBZ39A/PXyrZRISrIqxqhHngONiUHr69U7HEAoNbU4cOdOserocy8BZfyFaXM3YWLQx6PP45lww8InN93n/AxIUp2APRCTKDAQdWYd8Jr4eflmzkp/Q2nIUZt5j8Cf84WNcqymoRjx8m8ZoTR+WjB1Ftao2bdDx8Fm3h9+Tkg65RyNVnoQ4FH5ROjoaPuG3C8bgD4vGRuT655KwLcaUKVNw/PhxFBcXw+l0Yu3atVi4cGEkxhY2JIKhxSGo3gAftxsPr9/Z5XM+ueUIch7ehE8Kq9TkIcDb+OtBHhoTzyLJZgzaw9cahhte/Ra//u8B9QY18ZE3+L7GqbHdiYZ163B62TKvzTueY2A1cvjepDx8jd93Xi5XV9LxCf3TI5CGH2s2gGMZ1LRxyBpwLc5Wb45oU5Qlq5T7xOQ2BDzLQGofhOHxY/D24bchycEZHF9ngGUYPw3fLnhWMdmJSiLULgiQIKPtgH/BLnIPaA2+0g0sGWBsaGzaB5ZlYDawaHcKaLIL+LbYozm3OgVYDJza2wAALhyWgvo2Fw67q3eafDx8SQJYRtk8j5R+3hWcOhvfkThfsBp+XyZsi8HzPFauXIl58+Zh9OjRuOaaa5CXl4dHH30UGzcqXtB3332H7OxsvPfee7jzzjuRl5cX9sA7YtdvZuPDX1yEGLcu2mIXYBdEMIY6mDP/CzH19aAfVi3VzQ784wulONX6PeWo0TTvWP7v3R0uc/e6N+sGJlmRaDME7eH7er+n69rU37E+BjBU7C4RBT4bT77yQ32bC7Iso3XbVyj95Ascr2qGS5JhYFnIAJpZGfuMIsSiFjSc7djL1/XwQ9Lwvd8vxzJIiTHibJMD2dlLAQClZas7PFewlNa14cgZJb6djFq5PoMf5FyDkqYSbCvbFtS5Wh2CV/jks1uPqZ+zzcihye7Cis1HAAC7fnsZBiYqm8TVkPEtBLTuOgPZx7gJkrekwzLKBCiDhck2BXV1X0GWZViNvO7qs6KhXd1PIlyYmwIAakVIsrIhzpIoe6J0etbDd+cyRMzD95SM0OLR8Ht3RF0oRMRFnD9/Po4dO4aioiL89re/BQA8/vjjqqc/ZcoUlJWVobW1FbW1tTh0qHuzI9PizBgzIE59yJrsLthdImRXEuxnroRgPImPSz4O+bzaIlQZ8WYvfftYVQuKA+wdAIocwzLAZaPTkGg1Bt3n1PemjrcY1N+Fe8M/tvEQFr+0A6drPYba18NvaHNhS+YkNNkS8PXjT2POs9sgiJLXxt23ZkX2OfhFx5vletE9wUg6Hg/f/3ZNjzOjqtkBiyULqamXo7X1RERKT1/01Ofq/4lxJtefljYLA2wD8MbBN4I6V6tDQIamUuqhiiY8/j8ldNlm4vFdiUc3T7IZvdL7/wsXpGYn2vZ6BzH4SjpGnlXvd2vshbDby9HWVgyLgVNLJmvZX9aIce7IHEJGvBljs+LUUhcGPw/fE6XTkxp+pD18Iqn5hv1SD7+PQbr5NLa7PKVoG88DL6VizZE1IZ9P6ymJkozaVicYWcJFtdsxqvkIKhsDyzpNdgExJh5WI49YMx90aWFfnTLeYlA9knA1zH1lSgjee9+XYunru1DZ2I7fb/SejBvanPj1B0exbvAFmFhThKzmsxAkGTzHquNoZYGmZAOOfnsGYgdj0ovDN/Jcp5KOqFMemZAWa8ZZd9XEMaOfwsQJr3mlx3cF3wmDTILEIMgyi5vybsLus7uDCtFscQjIiDd7/e6se3Wo1xRHO/6dENCWaETTJ6cg68Tzk0gcE8+pkwpnng4AqKn5BFYjhxKfrmItDgGn69rUUEwtP548EIcqmnCootETpUM2bTVROj3q4bsn4Eh5+EKAKB1yv0VT7f+oNvjEU6pudmikChZJ0gzsPrsbZc2hhVVp45kb2pyob3UiOdaM7PZyjGo5itMdRAA12wU1y9Zs4HTrm+jhawxjzbxq6MMtz0AM298/O4Evj1Xjn1+X+B1DNm23DsqHyLCYd3oXmtpdMPjouGUJLOytLpTsD9wgQm/zKxhJJ5CGDyhJSsR4cpwZoiSHnRDjG/1BPudMt9Euq2/HVblXId4Uj9cPdrxRfLbZjma74NULQYutky5oMoCiMQkQGxxo2Vmp/l6UZLCMZx/HxLN47rqJuH/OCIzPGY2E+CkoLXsLNqOM0jpvR4TIiWmalQRh0cQBMPIs/vl1iRq1ohaQ6yWJV+oKN1IavkCidPQ1fGrw+wjkhv6ksArHqjy1L9JZxQPaXLy503Nol41aD7+h3YXaVidSYkw4ZR2MAfYzKKuq1zuF+7Uu1ZszG/Q7DOnhawydgoxqdxnbcCtu+mrq2k09Agn9rDfHYVf6aMw+/T0Kiqq9km+yEy0o5kXYEkw4/E2l3zkIouyvkxp5FuUN7X57CV6vEwMb/Fizt0Y9+YlP8OMg2/sFwrcpC/muhqQoRcaKqltgNVixZOQSFNYWotWlL+WdabRj6h8/hUOQvCQdLbYgQvuqkoww5Sag+fPTam0dlyiDZ1nVC7eZeKTFmvGL2cPBMAwGD14Gh6MS45K+9TsfkSITrEa/vyVYjbhh2iD8d0+5Kjtqy42z7r6vPevhR1rDd0e98f57RACVdHo37fXAtr8ClfsQY+JhMXDYdKASP3t7t3pIgjEd56Wdhw9OftCh3rvzZC2G/3Yzvj+lGHLS8CMjzoyGNhfqW51ItBrxi5sXgoMEx6kjAc/Votm4s4Rh8O0uESfdhZsCtc4LFt+3rtdQolqzMf3R4KlIcjRjStVh8CyretIZcWbUt7kw6vwMnD5Ui5Z6/RLCkiQjyeZtZIiHuvilwEZar1qm5/WcGnYrSTIa2lzq9xUMkiT7fY52p6/BV76DWLMBabEmNRnt1rG34sOrP4TNYNM9NylqBnhCSH0J1Of4o3svxu8WjAageLLxl+dAahXQ/JWyTyKIEniOUT8/3/DM5ORLERs7DlNS1sHAeu8XkVVbYoAx3XXpMBg4BuvdCYy+Hr6i4Ut4a+cpPLf1uO45upNIe/gBwzJ7aW/scIg+g8+wwGdPAMc+AsMwul4hwzBYMHQBTjaeRGFt4EbYpD74O98pmbRtDsUQDEgwo7HdhVaniBgzjylTJ8HJmYDSwJvRzXZBTWO3GDi0u/yTYfQgN3WKO7+grL4NDfE8hGxrxD38ykZ/g6/tjfpd+ijUmWIx99R34DkGv1swBulxJowZEIeGdidGnZ8JWQaOfqvv5YuSjDiLt4EzBhFaKkqKdqynzROD5xAkNSEtFH6+Zg9G/m6L1+98P1fthJBoNaoTv9VghZHz95IJxzUGXxulo0XPywaAkRmxWHpBDgDFwBmzY2EZn4KWr8ogNjvV6o5qUphPAhbDsBie+2vY+DrMGfwFAE+0UUO7y31tfYOfFmvG0vNz8NUxRZ4jm5qiBDUO3yXIeGT9QTy79VjA999deBKvujcsk6OSTh/AHA+kjgTKCgAAKTo6pVOQcMWQK2DmzHjv2HsBT5Xs9kYPVyoPLpEOshKtaGhzwu4SYTFw4HgeNYnDYK44DFHQL5nQ4t60BQCzgYUkB5cpSDabX7h+EiYPTsS+skaImVYII+LREq6H38nfL8xN9vpZYjkcGjcDU6sOI6G9CZeNSce3v7kMCRYD7C4J8akWDBiegMNfV+pOZoIkq6FuBG2J20APluA2+HqYNMlBp2pDz6LedECZnLx6Ffh8rndeMkz9v4FngvYsj2kMPomt9yUrwRLw9TzLgGE8Bi5ubg4seSmAO4nPyLPqKk1PGkpMnIZaYRquGPIJpElxEEYpm7QNHUg6hLsuHaauGsj7lWVl34BnGRytagYf/z2MyZ8HPEd3EakoNUKgTFumF+xXRJroM/gAMOh8oOhTYP1yvHexd9Nhs0Hp4hNrjMWCoQuw6eQm1Nv9JQCnIOHpT44C8Bgi4tllJVgUD98hqAWq6jPGgnPZUVTgr5kCSio78fKIkQumgJpTVI4x8qzqMfLFzYCBxftn670ycUOmg/v46BOXq3HZWmoungtOljDlmEeCMbsNg0OQMPqCTDRWt6P0sL8mL0iSV7LYu3eej1iNpBFI5hIlSXelBmg8fJeEZrsLYpIJMkLvhXC22bO60W6oWwwcrsn31Iry7QLVEUerWnDBsGTseWQOLhiW7Pf322cMUbNqAeBazXUAxeBoN7UNKRYkXTsSXJxSUtrIsWhzf2aBNn8b+NthYl0YZC2FmGVFncOlSjokik2PBKsRS6Yq4znqTsTSRukAAGc5BWPKZ/j01KcQpNDKjIQDWXFFqmtcIEkHQI9HJEWa6DT4lzwEZE4ADv8PKR/egd+meYxTrNkTx37TmJtgF+14q/Atv1Os2XVajW4g4d8tTgFGnkVKjBGSDFS3ONSwOFfmcNhN8fh+0wZd77bFLqjL7o6KWvmillHgWbVKINPoAtPsgphtQ0EIerXfuTWGy5CwC+bMd9SfTTyHFJv/6sg6dCgOJg/B6L1fqu+TTHrtThHD89MRk2TCtxtO+n0ORIZ45aZ8rJ8/FkO/qECcRnsONAF27OGTyUbEN21tcE1JgTTAgqYgitM98YFHztPmUGi/l/MGJ3i9xhBEVBGgTDjHq5oxIj0WiTYjGIbBvkfneh1z/rBk1VDPHpWGJxeP13l/rG74rVPwnjwDRQHF2HLxRdmFWGp5A+BZvFlRi2a7CxYDp2vgtFybPwiAIuNsPlCJ74rrwGpkUlfdhYDM4t4v7sW9n9/bpWTGUJEkWV0ZR6qJUIcGn+nZrOJIE50GPy4TuOMz4MGTwLDZuLH5FaRD8Thjzbz6BQ9NGIq5g+fi7cNvo6bdO5xQ68URGaLVISDWxKtLYVn2eOs2kxHFmVNQcewwSvZ5NojrW514/tPjaHeJSItTDCipcRLMDUsedu3DzQDIahIhxxuxo745wCs7RxvdwhvaYEjYg8FpTrxw/XkAlKxgX6xGDh8Nnor4mkq0fPEFAI3Bd4ngDCym/mAIzp5qxsk93slCLkEpQTtnTDpGpsTAfrQe6Y0ewxwoVFWU5MAevrvNXbtTxAiZA9PohJATi3qdGkBanIKEV7d7ShLf+Nou9f+kzMEfFubhpZ9M9r4eH5yH3+oU0OYUvSSbeB/N3MRzmDQoEQBwy4VDdM9j5LmABt/Is5g/NgMPXT4Kv5o7Uvf1STYjNhZdjrNViTDVN+ON8ho0O4WgooOIxp9sM+Gut3ej1Sm6PXzlM5ec6Wg5/hvkx92AL8u+xIfFH3Z6znDROinhNBGSJBn/3V0GQZTgVCUdf3PY06WgI010GnwCxwM/eAacLOIe/r8AvD18ALh70t1wik68uPdFr5dqv3xiasjGa4JmKUyMndXE4VjCGCSkZ+KL1a+oWv7P1+zBM58oG1tExyUeflCSjloozfsBXXvFOMAu4q2mpoCtAztClmWvCpdDLBcAABLTDmPB+EwAwKBkf4PvEmV8kT0JzakDcPbJpyA7nX7vZ+S0DCRmWLHj/SKvRCynKKkFuczDE8AYOaSVejzrQJ+HS5QDNqwmYZ5znt0Gh0sCd6YdcqwBh+s6bkHX2kGxuze/KQEATB6cqOZOEAxc57V/AM97MftEz+x/zOPlG3kWWQkWlKxYgBnD/eUzIHA1UaeoePg8x+KuS4ep34EvKTFGtLhi8cqBpZBOOlDlEnCYlfyqbOpBngHtBMf5BkLIJnz+bR5GJY3Cyj0r4RKDL/vdFcielpFnw+p78U5BKe5/dx/e2nlK3ZTWK0bIcUyfbHQSiOg2+ACQmIPt8QvwY+5LDGSqEG8xeHlMQ+KHYMmoJfjP8f/gcO1h9ffaNGvyfbfYFR1eG91gMXqiJJpdwMxblqGuogzfbfgPAOCgpk9otrtGilYC6Qyn6LnB/3XbNPX3w5KssJxoQgUkvFyqeNKvby/GXf/6PogPRTFI2s2oUSlDILZnoVLwFJbTix0fnRkLgeXRfsfP4SwpQd2//+3Zk3C/H5ZjMePHw9FY3Y59n3kqqbpECQa3sWAMHKyT0xB7shlJ7ik10OfR0OYMGFFi0jSybnEIYGsULf6zumbc+VYBPjuiXxnVt76Mtl3ep0eUfR89I2rglAiVt3aewrjHPsL6PeVqmQQtjgBt+LR174MpfmcKUFyOePidMSozTv0/W+PAcLMJhyxywAlCi5FnMYk5jswzno1ZltUrmc3iF5N+gfKWcrx/4v1OzxsOLe7yJklWI5yi1OUNVRKRVt/m8mTa8v6rSOrh90E+TfkJJLD4Bb8esSbeb0l+18S7kGBKwJ93/VnVnbUePjm+2R1p42XwNR5+q1PE0ElTMGLahdj5/juoqyz3Ki9MlvfESOltOjkEEQ//Z79aLuBQRRMsBg4JVoOXF8gwDLLbgSy7jBUnK1HY0o7HPyjE5oNngvJIWnzq11+el4HRcRejjSnGqaZTAJQNqzV3TMcHP5+hHjdrVDq+fOBSXHLTlbBddBFqXngR1tYmdeyEQXnJGHV+BqyxnkgQ30bRMRdmAZKMq6EcE8jDr2qy62aFAh4NH1AyYJkWAbCL+LS2CR8dqsKjG/RDZVud3u+/3SX6bYDrNckmm7aPrD+IZruAe9/Zi9e/LvY7TvXwDYEfsWAMtuLJ+n8uwRp8bZw/A+CaxDi0GBjYkwNH6Giv/Uv+XVx4bAU4KGPgWAYn3HkgkwYlqMfOyJqB8anj8cqBV+AUI9+ngUDyQga55cau6vjkuzawjCrp6NVqYhkapdPnsJvT8JZ4Ga5mtyFbPO3nMcUZ43DveffibNtZVLUpHqE2REs1+A4BMSYD4i2eh4UYhTi3VGR3iZh58zJwvAHvPfccpsscJkI5JtGt/ZPldJvTX1b4/Eg11n5Xisf+dwgHyxuxZtdpzM1L1zU+NiOHvFoRcTyHuwtPQXYPuapZv/G1liYfg58cY8KLi24DAwabTm5Sf3/+sGSMzozDj87LVg3/4GQl0Sj917+GZLcj/h2liFi70/tznb10DEadn6n+7BJlVdIBlKgT5MbjShhgQmAP/2yzI+CmpNbonaxuQaLVAEuDE5UGQGY8nZx80ZN0Wh3euRFxOrHzwZRzBjyGqKNG28F4+IGu5xSloPsh7PrNbLy2NB8AkCuwMDslVKQaO80D4VkGb4lzEeuowkXsfgCKASSJZ2M0qweGYbB84nKcaT2D9493n5dPDP7AcA2+puZ/R5IO9fD7IAaOxQvCIrQzFlx19kW4BP8vcFHuIqxftB4ZtgwA3lmoJCqgxeFCnJn3CmcjS2Mif1Q22hGTlIwZS25ES3EhHpA53AbFOyXljLVF3Xwhsc9N7QIKK5sgy8CdF3viwDcsvxCv36w8vCYDBzglPDNqIApb7RCGxQKAV/XLQPi2WIy3GJBuS8e0zGn4z/H/wCF6smU5lsHT10zwK7ZlGjoEWU//FcwtdwLofE9C6R7lvWy2zshCAlhcDoPu62VZxtkmRwcevucW3n26AVYDh8EuBjCwkNIsul4boN90vdnhUuW+X80d4affA4Fr//iuqsjkpTdRG0UH5pzditojnXfoCqjhB+nhA0r12PPcm8MVDXaknXGg2cTivx2UAgEUI76dnQw7F4MfcEq4Mcsw+MWsXAD+kt/5medjUtokrDqwKqSOYKHg6+F3deNWLTHOaKJ0dCQdquH3QUw8i3rE4U3jtchwlMAm+N/oLMPCzHtuYG0pXy9Jx8x7PWjEgyOFtUgjlAlz56MlJgNlTbsxCTwGwHMzdWTwyQTS7BBU46MtlzthYAJmjUpX35fDJWJuSjyuSIiFOCQWko1HRQdVOwm+GjbJgL1j3B0423YW7xx5R+9lfsTNnQtLmhJj/uaOErz4xQnd4z49XIXyhnY/LyppZBIcKWYsgRF2HSPcZBfgFCWvz0CLVtIBlM9vZlIsmFYXhCExfklUhFaH5/dEomt1iGqBPD1jDwSOw/c1PCTSR8/gMwYD0p3V2PPOG5DEjg2WMUBYpkOQYOQ71+EJCVYDbEYO3xXXoepwHZJcwONFFZ0m7zGcEUcSLsZctgAGCGAY4P65I1GyYoHXag1QJoj7Jt+Hs21n8dK+l4IeWyiQfAmyHxaMc6MH+Q7bnVKHYZl8EHWD7C4RhzR7db2ZfmHwiYHeYl2IF8auwVkxttPXuDSxty5RqdWizZYlEAOd6dbnyWYQy3LYN+gyFDsVPXwWPAaESAV6Bp9MNK0OQdVuA3ly2g29mxLiAUGGa0wCmnTO6wvxcG92p+8nuGWqqZlTcX7m+Xhh7wsobQquSToxal8dr8FTW47qHnPbaiXz2fehYhgGtkuzMRAcEo77PzREeglUmsBXFmEYBqPT48CfbIYcb8SRAImsWkmHZFS3OFzq7wNtaho0ZaG9zue7J+AUYZABk57XyBvwTeJ0NFefCZioRzByLHafrverD+QUgpd0AOVzSYk1YcuhM2AADDnjQJVTwJ9OBi52Byj33t742Yhj2rCA3ekloWijdYg8NCltEq4efjXeLHwTx+ojX3bh8yNnMSzVppYpuf7Vb7GjqPNua76Q77nNpemGp1urie10FfGr9/ZhwfPb1Qzm3ky/MPhERjCazGAMVi9jHgith69o8xIESfbz/IanKZMHWd6e0RQgqzCk4vTc23EIgpfB5zkWMSZe3+BLZDXh0o3B12LShKZJdhH8sUbISSZ849AvXqaFSDq3XzQEJSsWeE0qj13wGDiGw8PbHw4qgzKxgxR9X/S8KGteMr6HgJzDDZB84ufJPkegMEJf0+sSJdhMPNiKdrBV7TiZwuPbBv8QzWatwY8xwQABzXZBfbh9i5ERjDyr20HKd/+htcGOW5pMqNnrn3G8fGYuiq2DEZuaht1bOu6Na+I5yDLwo398gzfcm8Oiu+BbsJIOQfvZ15Q1447sFLxeXoOv6gLnchg5Foctk3FYGojl/AY4HB6jpj3fohe+Vv9/33n3Ic2ahhP1+qu9rtJsd2FfWSOumpTltXI64s4EDu1cynfY7hTdwQT6tZriLQbd51RLgbuBTVuQJc97kn5h8EkMu8XIKZswQdSw0S7bXaKMZofypcf4eJpEarAYOZh41uvmaHUIiLeZsBUCRoCDq8oTcx5vMaBRJzmIXLeqyYED7gYlgQ0+p0bG1LY4wZW3gT/WiKwgHA1yw+tJFwNiBuCR8x/B/ur9fvkJethMPB774ZjOLwr/eiWAYsz/Djt4QUb9hiKvzUQivQQywKTi480X5CA3LQbPLZkEq4kDA8BwsB4Gh4Q7DpXgjMNnItEY7TwUY5vpXvDl36kPbUCDrzN+7TgJLiODOk7GkY9Ow9HuPUEsn5mL4id/iEnzfoCywoM4W3JS95wAvNoQ/sEd/jn8tx+ipsUZck9j7YrghmmD8euhAzDMYsK9R06jKYC0Y+AZOETg78JVGM6WY37zOvVv2tDl/WWe1VmCOQGbrtqE+UPnhzS+ziDlmjPiLTBr3nuw7UK1uNwyTZtq8PU/ywSr/nOqhcwTkSjmdrbZjpIOOueFS78w+GQzhne3ZxMkudMIBa1u5xIlNYyR1H75390zsO6n53u9RmvEZVlGq1Mpp/AJXBABtH57xvtYHc+BLC8vyf4aBscGGHk2YAcnbdp9basTDADL6VZIjs49DWLwA5XnvWLIFbgq9yq8euBV7KjovL58ZgdFwDqD51ic5oADg61o31eNtj2e+ketnXj4CVYjjvzf5fj9D8dg6/2XYOLABFjd3h8jyEg92owWUcIdB0vUhxyAWoMGAPa1p4CBjFH7/oQ2twdrMehfL5BhaHcp49xy8Az2ljbA7pLwldkF0Snh6M4zuq8ZN3MuTDYbvnjz1YD342BN8tu4rHilDLRMxhJaZy+iuVsMHH4xezisHIu/jx6EOpeIgkZ9I2PkFEnjQ2kaPhCn4xbn20DJduV8ATbElbEFrtPTEQUfvI9VP7sFhV/5F2WraVG+m+QYIwZo7rfnPzuB2pbOV7VaSLkExcOXAxt8i1HtCUHYfKASr2uytAmR8PBnrPgcl/71i7DPE4h+YfCJZ8NzSmYi0HnJUxKne/MFORAkWQ1jJFryuOx45Ockeb0m3mLAvrIGfHOiBnaXBElWvN/PHrkM1gmpaP3uDMRm5eYZkGDG4comCO79AQLxEiakHsSlWf+DxRB4nNp+sPVtiseXEmNCTYuj03ovzXYBNiMXsEYNADw89WEMiR+CX3/1a7/SE75cMiK1w78TAlUINRtYFGSaYMyJQ8P6Igg1ysYzKUndUSkAs4HzmhS1k4OrwYFnRg7Ed02teLrEY3jbHAKsRg4Lxmfi4UX5eNK1BCmNBxHvDikM5OETo5np07KQePg//df3uPKFr/H+nnJU8TJSBsXi4LZyXYNujonBRdfdjNJD+7Fnywe61yOlFwDFo9Zu4HbUYU0PsjrR5gacF2/D9xeMwaxk/fBVI8+5DRmDX7tux2k5HXj3JqDhNAL4IWFhS0hEW1MDPnllJeoqvDvSEQ8/2WbEwCQrjvzf5ZjhLvCnV9q7I8gqv82pBAUENPg2A+rbXF7f311v78bjHxRi3/ESyKJLDccIp9QDIVI1/gMREYO/ZcsWjBw5Erm5uVixYoXf3x0OB6699lrk5uZi2rRpKCkpicRlg4YYd4uBU5ehne28E8NEJJu61sA9SAnxFgOOnGnG9a9+q+q8MSYOSTYjEuYMhizJaPhAWb4vmpiFikY7Hli3H0N+/aG6LCU34melFyHB1ITLBgYuP6v18EkLRZuJxwf7KzHid5s7bJTe4nD5yVO+WA1W/PWSv6LF1dJpw26zgcPPLh3W4QQCePYo/K5l5NHmkpC0ZCQYnkH9ekX/bVM19c5LAajn0kwOrU4RV6Yn4rrMJBQ0tqrfe5tTRLzFgBeuPw9ThyThE/5ilNnyMHz3ExjCVAY0+GSzb5BPnSHfnArSL3jsxVmor2xF2VH9EMjxs+dh6OSp+OLNV3D6oH+Y5pScJHz5wKWYNSoNLlHy2ivoTFv2hWj+vtp/UoDVDKBMEuSazbDiduf9gCgAa64HJ0Q+9HL0jEtx2/OvgDcYsen5v0Bwed4jeQZJEx2zgcPtFyk1iELt7yxq7gNBlAJKdQkWo7qHp+VSdg8mvD0BD/z9X6hwTza+jXPCIVKF4XwJ2+CLoojly5dj8+bNKCwsxJo1a1BY6J1q/tprryExMREnTpzAfffdh4ceeijcy4Y2Rvfs/MMJA9RlaGd6myBJ4FhGXR3UupeTHRlJ7WYeMQykGqIhxYK4WYPQvq8aLd9WYmiqkrz0/h6lqxBpW0hm+IM1o/F91QTMGbQR7e2nda9nMng0/DangBgT55WVubc0cJy1tsduRwxPHI7X5r2Geyff2+mxRp6FKMkdrp4CefgWo9IUhk8wI/nGMUhcPAKAR2sPZID10B4rSjJcooQ/Dc/GOxOHqZEYbU7R67h4qxlvZDwCCRxeNzyFROhvBJ5tUr6nrERvCSuQdzdyajpiEk34et0J3XhuhmUx/+5fIWlANv737Ao0nPGPmhmcbIOBU/aeyHXGZMbhyR/5V9fsCOLFdlYlU4uRZ72kipPyAGDxa0DVQUzZ9wg676oQOrFJKZj303twtrgIHz7/F4iCcg/Uqh6+J0TXt9F6sBCb4BQlv4RALWSPyFfWOSYppaOt1XvU30XCwydoO81FkrAN/q5du5Cbm4uhQ4fCaDRiyZIl2LBhg9cxGzZswNKlSwEAixcvxqeffhpUt6dIcfMFOXjrtqmYMybd4+F3snEriEqFRnJDPbBOyTSM68BIakvsEgOurVMeO3MgzCMT0bDhBJKrvGPlSUVOMq6UGBP+fWQxABYnT/5N93rEw5dlGa0OAVYj73XTBVrmlta1YfPBM0F7ERNSJ8DAdj45BHr4vDfA9R9Ms4FTDYtpSDz4BOWhbnX/zrejU0f4rgYcggQLx6ot6wBlgtQel2gzoFhMwfsj/4JMpg7JG24EnP66NqnlMtBdCI+VSfcl/fuJN3K4cPFw1Ja14NuN+puzJqsVVz7wCADg/aceh6PN33Pm3UXbyPd75yVD1YznYCHOSyjRPQaO9b9Phs8BLvs9BlZ+hCVc9zRAyZ0yHTOX3oHju77BpuefgigIaLEL4FnGK2RW2wAnFIhT4nBJcIqB+y2QaCBfD78CyTgrJ+A81tPisSMNf8PecpSGIMHVhLgnESxhG/zy8nIMHOhp3JCdnY3y8vKAx/A8j/j4eNTW+sfOrlq1Cvn5+cjPz0d1dbXf37uK1cjjouGKxkw0fL3QzBNnW/D2t6fw07e+x8vbTnq1kCN0JOmM0aTxV7gTsLSGimEZJF0/CoYMG8T3izBS8/ETI0kM4pwxGWhwxGPrqYtwpup/aG31NxZGTul4JEgyWh0ibCYOozM8Y6gKYPCf/liJlS+rD70lYEeQJChfg6+dCAM1bLEa9fv8tnUSFx/oXFr0ztvq4+EnWIyob3NiPzsSv+XuB1O5B1h3qyJfaPjN/NG4+YIczBmTDqvLjpc+/QsuL9kJVweFvIadl4oxMwZg95ZTOLJDP+49ISMTP7zv12g4U4EP//4XSJL3mI0cq3j4zs5LNgSCeLGhxO9bDJxXGY6pQ9z7Vhfeiz2jfoUPxOnq3yLVcpBw3vxFuPSm23H822/w0UvPoc0h+N0HakXPED184lg12V3YtL8y4KrH4yB6zq9ISgy+ksZiFrsXRiiyUyAPX5Jk3LN2r1foamdUNfVSgx9Jli1bhoKCAhQUFCA1NbhNwFAh1Rr1PPyf/ut7/Pb9g9hySNnc4znGLxKiI0nntaVT8HN32jlpt+fbw5U18Ui5eSxYqwFPwYp095YPkWbIQ3PlxAEAgI9OzQLLmnCy+Fm/65EibF8ercap2lbYTDz+tmQi5oxJA6BMYHqQPrXPXzcp4HvpCsRzdPhkj770RZH6f1cAo0j6/PrS4hRg4JiQvVItevpuu6/BtxpQ1WjHml2l2Gu7AFjwNHBsC/Dhr7zqbGQlWPDYwjxYjRzMggO15jjcs3cdLAd2B2wqzzAMLr5uBLJHJeLzt47g1EH9RKFBY8dj1i13ouL4UTSc8Y7s4VkGLlHy1OgJYQIkEEMfSjin1cSrzWR+t2A03rptqvIHhsHhIUvRAs9eRjBRKkfONPmV9eiIyQuuxAXX3IDDX30Odv9Wv4nO2EUPn/RzJk7P8QDPCrmXtOdPtBpw2eg0bBQvRBzThktYZe8l0IqZ3H8d7an5UlTdcXnvrhK2wc/KykJpqScjs6ysDFlZWQGPEQQBjY2NSE72b/l2LuA6MPi+beJ4lkWaT72QjvTPJJsRFwxTogbIF5Yc418SgIszIuWWPNRABFGCHYKEhjan2gc1270p2OyMxeBBt+Ps2Q9RV/eN13mIR337mwWoaLTDZuSRHGPC73+YB5YBtp+o0dU2a1scWDw5GwsnDAj4XrqCifOXdGRZxtdFNWotnIEBertqJR0tJTWtAV8TLPoevgCr5vtOtBrVzbei6lYg/1Zgxn3A928AO1b6vd7AsaizxOOx6behLCYVQ197Bm1nA2d8chyLK+4ch6QsG7a8fABnTuqn4k+YMx+3PPMPJA3wfoYMvJLhSybFrnj4XZF0Ykyc+n0mWo1epSxkH/2+s3Lf7U4Rl//tK9z3TuAaQruK6/CXj454/W761Usw+qKZsB3cimEt3slcRp17Lhh8gzYCScwGHQnYIUiIMxvwtZSHGjkO13GfAQj8/gM5An5j0kwqR890vbFRR4Rt8KdMmYLjx4+juLgYTqcTa9euxcKFC72OWbhwIVavXg0AWLduHWbNmhUwtry7IQZbL1pkeFqM18+iJOGi3BR1gzUYSF0W4l2TtH1f2BQzjg0vRSZfjHSmCTVnKnHlyu14t0AJRdMWCxs8+KewWHJQWPgrOBye+u6+nhrxWLMTrRidEYcmu4BrX97hVUZAlmXUtDi9EnoihZ6GX9loR1WTA3fPysXrN+fj/jkjdF9rNXJ+kS6yLONAWSNGZnReCsOXN2+dit/MHwUAuo0y2p2iGq8PwKvk9f8tylP+M+tRYMwiYOdLgMPb41J76fJG/GXydeCbG1F7x61Ibg9cU8Vo4fHDn0+ELcGED1buQ3OdvuRmjYv3+53B7eF3VJStM0g+SigGX7vPwfusdonNtLnvO9/yEr4cdmfEHq4MnBl7zcs78MLnRV4b3AzDYO6dv0Br0mCk+rRRNHTR4PvKb4HiDPSawDgFCSYDCwE8XhPmYxa3F1OZwwFXOMFGELVqXh9oxREuYRt8nuexcuVKzJs3D6NHj8Y111yDvLw8PProo9i4UUkbv+2221BbW4vc3Fw888wzuqGb54qOwjJ9H4Q2pwiWZfDp/Zfg/pHZeOOqCZ2ePyfZhlgzj0MVTcqyP8CDybIscgcNwEi+GleYjqLg43UY21IAGxTtTruJxHFmjBu7EoLYgj17l8LhUPY3fFcP2gqQZD9hT2kDrn/1W3U52exQ4o71+tWGi97ymjzcYzLjMGtUekBjk2Qzoq7Fe8m76UAlKhrtXnXXg+XiEakYnq5MFNoCagfLG3HxU5+jstHutaJL0JSH+Mn0wcp/WBa46mXg9q2AydsZIIaAZYBjiYOwZ/ljkKqq8PS2lXj2gkQEwhpnxMJ7JmLC7IGISQj+OyBF29rDkHTImEPR8LWfkW/l0Xlj0pESY8RdlyrVXDsLAiisUO4F35BWPVp8Jg/eYEDWkOvxU3YcxFaPJBTupm1n8GpUn7eHT1Y6r4uXo1xOxl8NLwF2/zIaQPAhlsQxe2DeSKxffkFQrwmViGj48+fPx7Fjx1BUVITf/va3AIDHH39c9fTNZjPee+89nDhxArt27cLQoUMjcdkuwXcQlin6yDxkZnbaRdgONqHuyzNeLfv0sBg5XORuVJIUwLsHFK9lzpw5SJ/2Q3zkHIGc8y5BHOPA5cYjsMIJhmHwf1eOxcs3Kj1VY2NHY8L4VWhvL8P3u69Fe3uZ1yYxALUxBeD9oB6pbMLil77BloOVePlLRU/vFg9fx9siKffazkt6pMWZ0OwQvJbFx6uU93PzBfr9XjtDr3fw858eVxOWtBp+osbD91p9GixKj2QfyAaogWPBMsDZYXnAsy/CJDox/IlfYlhDubqf40tcigVTFgwB00nOgu/1BFEOqs5+IALF4XeETfMZ+eZYpMWZUfC7Oep92FHk2+YDlfjquOKoBNNPV68A4B6jDJsENP7PsyfU5bDMIA2+0b0q0toLUsdoeFoMHDBiufMepDP1uK7wbqDZv8Oa1sP/pihwAqM2x8O3Cmyk6FWbtucCPU2OEKiomsnC46Ilw3HmZBP++dDXeG9FAfZuPQ0pgFcRayLNnzs3qldMGYVKKR5x2cPxkXMkTIyAOcajaGtrw43TB2NeXoZ6bGLidJw36U0IQgva209hgE+2p1aT1xq5t26bhupmB+7612688LnysKTo7C2Ei97Dt/lgJabkJHYY3QQAqe7xaMPRnO6iVqEWCSOQjNLviutVHVVry303bYHgvV/iWS6aOEDtc+sYOgK/uvhuyCYTXtz9Gn42oPPCc8GidGaSVNkgHA0/lDh8bw9ff4LSkz20OAQRd729Gx8dqnL/3Llx1ksqOwEJX6XyaNtbjbYDiuHsbNO22e7CmztK/HIgAiUA+kIcRHK8LMuKpMOzeOWmfHxy38V48/c/wz14EImOMuC1OUBtkdc5tJLi9yWBc2PUEOQgJsSu0u8MPgnL1AuR6shDGTElA/PvGoecCSlgWeDrdSew8fm9foWxAE+mZ5wltNj1WtmGz1zDEcc48NZbb6G93T9sMj7+PFx4wRdISrrQyxN9bWm+l0ep1VunDknCu3eer8b6A93k4fsY/Ca7C8eqWjBzVFqnryUZzWc13bpCLQHsC/GSnt16DCs2KxuBjKYvgVafHp4Wi/Q4E56+pnPZDlCM3O5H5uBPV42DgfN43+UxqWhd8XdwsbE4feutECIUXkyMKqnpFE6UTmfZ0Fq0EzUXIBtVL5JFy97TDV4/61Ub9aVJ57lqd4n4foAJhuwY1K87BldNe6ca/ivbTuLRDYewYZ93qHiwCpDn/IptECSllpGJZ5GTYsPw9FjEmQ343jgZrw59XsndeG0OUObpLa2VFH2lKi1qsmYIOSeh0u8MvqGDm9131r99hreUMGRCKmbfNBo/ejAfs28ejcrjjdj4tz2wt3p7I+QL68yrBTSbf+4b9owUh89duaipqfHLZyBwnL8Gmmgzek0Av5wzEoDHcx2dGYdx2Z7NwO708Ml7KXJvPJES0h1BxlPd7NHxXaIUMAMyGLQ1Y4g8FMjDH5hkxbe/uQw/DCFyKclmBM+xSmNzTcikITsbg99cjdR77wEfofBi4qiQEMlQK2UCHhkqlHAJ7WcUqFgaWTUHSj7zTQD07aeshawimnRCN9ucIswmHsk3jAZYBrVvHgLT5gLD6K8u3v2uFB8XKqsK334CYpAevqoIuI/3lCz3CQ/lWBSbRgK3fQyYYoH/3g6IAprtLqzf43mOO3rvLT7Z+d1BvzP4vMZj9N1M0d6w/7ptGn73g8Alf0dNz8Tld45FTXkLNvxtD9qaPIaKePjBLJ09RtIzljIpAffeey9yc/U1YC3kgfSdXBJtRtx58VA0tLnw14+OQpZlzHBux694pZNVR/sLXYVMdCRag8T75/pEP+m+1j1+UnUSiICHr5E9Et3vV7vKsUboweLdTVHI/WQ2sDBkZSHp+usjcn7AY3ia7QIsPsXigoW8guugyqUvXh5+Z5JOAC/b1/PX6ydMICtTPQ2/3SnCYuDBJ5qR/JPREOsdqH3tIDJZzs/DP9tsx4P/2Y8j7vDG6sZKHDn6KI4e+wMcjrNq0IbMMxByYjAxQGCAr1xFruMrM/LuSR/Jw4DbPgGufRuHqlqxZNVOvP2tpzRKoNXNhr3luPMtZVVADX4E0d60vunL2jhYbZheIIZMSMX8u8ZDdElem7nE8AWzdNZmp2oPt9mCCwUlBl9P0yURQis/P4GmdgFDHYW4m9+AC9kDIem4wUKSzEi0EKkH4tv7VA/ijWtT2EPp26p7Ts1r1Y5ZWg+/Czq4HkZ3BE1hRRNYJrgolFAxaDz8rsg5gMfTvmBY8Dkw2knTNyzTd2yBdHFfqbS5A4NP7uMvjnpLYZLkLjfudqbMwxKQvHQMhDo7XhKtiK/zfpaLznqyuxlImJn2V1RUvIPy8rX4ruAqZFiUvCAx3QJhZDxOTojHlbuPY1N1g9d5eJ/VC3HMfFdYXq0vY9KA9DFY8Px2HKpo0hzDBPTw71m7V/0/1fAjiDZzttYnDFAbqhkfhP4OAIPzkrHkkamITfIYNWK82CC8MDIehyAhwWqEgWPwwvXnBXVtAPjZpcoqQM9jP6spwOQQRKzirscJaQCeMfwDaNav0R4OpBgb8c4CPRx6WAz+ETVOMTwPXxsSSyoZenv4kXmwDByDHUW1WPn5CeQNiA+qKF3o11A+B+Lhd4VlFw/FvZcNx6KJwctW2u+us01bZwBJx1ducdoFnDnZ0OF1SUQPodUpQJa9W12acxOR9rOJcDAyFhxqRusu5Z7+3foDuO6VnepxUzL2INVUhNGjVmBK/n8BAPdMehYjE4+DK2/DHM6M8+KsqHUJuO1gCe49fBp2nz635D2QDVhfR8TAsarOH4hkmymgh681FVTDjyDaWGJfD197Ywaz4UpgfYwSSdoLZm+MYRilN62gaMBLz8/BgvH+YYCBuHWG0qJQbxl41yXD1LA6hyChUeDxVOxDSOEdwDs3AkJk63XYjBxYxuPhO9ySDBvEB0GMs7a8QrgePunEZTVyqlep1W5DKbncETzHoryhHZIMDEqOvHevXMMjdWj3JkJhQIIF9142wkvW7Axvg6//OmMnko6vwb+s1YD//X0/mmr9gxJIpEqzQ/CKrAnUoc2QYcOvYwWUxnKQ3df/187TXsdUtqbjUMNsZGQsQmzsaORPfg/19gT8Kv8fuOeCYrw8PRf/HDcUn00ZhfsGp2PtmTpcuecEKuxOz+pFrZ+vX67byDFeKxy9yqjJMcaABl8rnYVSGTZU+p3B13r42g//yJkmtaMO4Ols1RUk1eAHp7OaDRxanUo/1Uh+2YOSrVjhLqF7uLIJlY12ZI7IB3f1P4CyXcD6n/kVBwsHhmEQazaotVIcLinozUVynJekI4Zn8AHg5guHIG9AnLqU1mZD2iL0WWvlsbhOegx0FaMq6fgXEOtOtJuTBp2G7NrfBwrL9NXwt5sVY/7RqoNeAQ+CKMEpSIi3GCDL3tIPeVb1mtmLRg5rBplgO19xlHz3jEqbs/HMrkU45m4xajYPwIpd96ANIzEh5lmUlfxZeR8sg4eGZuKNsTk43mbH3IJj2NPa5vXeSDa4r+ziJelAf9M5OSawh6+1N91ZhaDfGXytd6Pd6Ln8b195FRsLxisNBKm1HezeWGqsCWX17ZBlwBzhh5kY0mXuDSGTgQPyrgJm/x44uA5460qg/lTErhdr5tXqig5BVAu8dQZZ6Wjr+XfUazS0MRnUB02b2BUpw6l1Ijoqnx0OZD+ouKYV1g4alkQa7fcX6LodJTMC/hp+Iydjwo+Hora8Fe88sQuHv6mAJMlqsxvSTYxIg+UN7bj/3b0A9Hsw5yRbUVTTqhrKQI/uvL9tA+DeDxBsKJGfxOBBd8JiHex13BWpCdg8eQTieQ5/L6uGDEXDl2UZT25Rwnv96m5xDFwaSUevUFpqjCmght9ZM6JI0e8MvnZJrxc3fN6gBGy9/5KwrjFndDrizDyWXpAT1PEZcWY1bDCclYUeJh+9Vy2/cNH9wKIXgYq9wIvnA9ufjYjEE6fx8O0uKaSMQYuRw5mmyMXhE2JMvDomIhktmTIQmfFd78OrRTsp6XmgkUAbRBBpp6AjtCs0s7FjSSdYDR8AbDlxuPKXk2CNM+KzN4/gnSd2ofiAUnyOGPzGdhd2n67H5X/bhoPlyuanXqjziIxYnKxuVa/DdBJ4SvbqeM6A3NwHMTD7Jv9z2szYnD8C/xg9CIz7PZTVt+M7d+KUr85Oku8I9W3+Bj/WzAfMVUiwKHtwN04frPv3SNHvDL52uUQ0R22lvJQYU1BhhB2REW/G/sfmYZSmNn1HpMeZUe6un6+t6RIJfCUVr046k24AfvYNMGwmsPUxYNtfw75eVz18AGhoc2HD3gpsO6Zs2IWr4RNsJk7VhlscAhaMy8SKH40PKQGpI7Qefij6eChcOCxFzdy2dFHD7wraCTvQZjGRdAL1OtAaOfJ9NjtcyBgSj8UP52PeHWMhOEVsf60QV7YYkWVU3mdTuwtXv/iNV40oPclsSLINTlHyClLoCFJWoTPJNY7nkGE2gmGU96aNUvKVXo0+ko5vQAjJGA+U3MmxDHLTYvA4KdzXTfQ7gz8sNQZPXDkWgOdG1KZ6d0e4YmdkxHuSoIIJBw0F3+JtfnsECYOAJW8DP/kPMP2usK9nM/FocwqobGzHB/srAybrdAQJZXOKckS+D6uRV6WcpnYhpA35YNCOsbsaubEsgwkDEwB0raxCV9FOuAENfielFbRSB5m0SNN3hmGQOzkN1/9+OobMysJggUV6tbIa0yuvoCfpEGmOyIGBSqQQiOQaKOpIC8MwMLAsnKJ3607flQbJtiZoPfyUGBO+fGCm2tNAD4cgIiPO3O1VhPudwQeAa/KV7lsuUUZpXRtuen0XAGDKmcOIa2045+PR9ugkS7tIofXw/3z1uMAeRO5lgDUp7OtZjEpd+3vW7kVeUyEu/G4ltr76YkgtLcmk5xTELmWU+mI1cu6wPhlNdpdfU5pI4lsjPpIQuehcbtpqV0GBVi/EcAYj6ZDw4RaHx5gvf3s3hj2yGbutIt6IdSBtulKKQ2/jUy9c2jdbXa8cNsEpSGqRxGBXeEo/Ye+OZr4hvWrilZs692Z0we8uw85fz8KABIsyKUiy7rPgFIMPcAiHfmnwtbHvf9t6HLuK6xDvaMZvvnsTl3zw2jnttwt4b9hE2sPX3kTXTR0UccnIF6uBQ7tTRHWzA02GWDQYE7Hvkw+xf+uWoM9BvCdSPC1cLEYOsqxEuDjdzSsiifZBH5keeu3+YCGrs+4oixEODMP4SRpatOGKxOA3tQv4w/8O4YkPCrHpgNL28dXtxWjiZAxwh7bqefh6k51vDaeOmon/6r196niCNfi8+71p83R895aMPhp+XasDZgOLlBiTOlGSceqVoHC4pJDkz67SLw2+9gZNi1MenkZTLFaPvgKDj+1G4/vrz+l44rrT4J/D5T9AGpmIaHUIKLUMxH/Tf4jB4yfhi7deRX2lfm0gX0j7OZcgR0TDJxm1Ve4N4UiHThKZ4dYLh2D26PSInlsLSezx7cLWG1CiVAJo+BpJJy3WjASrAau2ncQbX5fg1e3FfsenxprAsQwa2oJrhUj2GYiHr03euyY/2+vYjfsqVE89eA/fX9LxlV60YZmtDgHFNW1I8nGuyErIJUo422zH8rd3e0KYhdACHLpKvzT4gOLlOwVJLcsLABuGzcCpAbk4+9RTEOoDlzGNNDEmg+b/EY7SOQfLRC0Wt16uxrszDOb99B7wvAH/e3YF2hobOj0H8dQiEYcPeGrmnHGXFoi0hp+VoET7EOehu2h3OnGJ4QQc5Uc6P/gc4xuHrkX7e5uJw/xxmWqQgh42I484M69+X53hkXREyLLs5eE/tXiC2vkMUDzxUDR8QMmcd7jEDqvpGnhG9dyveH4btjf8HY60Z1BYW+g5RpPE9dIXJ7HpQCXe+a5UHTuVdLoRI6/coNrvXGZYrL3gOojNzah64o/nTNrRSjqR3rQ51wbfauSU2vCaYnCxySlY8IsHUF9ZgTd++TN8+a/XUXb4ICSfZucf/HwGAM+S1ylEJg6fSCFnVA8/sgY/P0fpcNVdIZmEVqeMWMaBpgp/r7inMXBswAb13gaf77SGkdXIIc5iQFkHk4IW1cN3SXCKkt/G+R0XDVXrGyl6PMmTCe5Zsxl5tDoF1cN//rpJfsdoJ7xy+z4YEnbDyZ/GzVtuxpelX6rXBhRHxuIOcSWb1w6BavjdilL7QvLT045Y05H687vRtGkTzjz2Bwh1+m3LIkl3Goqu9D4NB2JcfT/XnImTcd3//QVZI0dj94cb8M5jD2PTc095HTPA7Sm73BmXLQ4BiRHYcyBj+tCtFSdGuFLognGZ+Ndt07BkyqCInteXG6YNQpmUgKbaM9i8eTP27t2Llpbu6X0aKsYOJB2twY81852GrlqNPOItBlQEafC1TVDsOhu2DMOoIZUGnlUNd7AevtWkyJQk+kfvedUafLF1BNpO34rLbCuRE5eDuz+7G3dtvQuHmz8HOCVfgCRukcqyiobf/c/quUvZ62UYeWWTxTcRosnuQvKdd0Jsbkbda6+j4b33YB49GtYpU5C87A7wSeFHsvgS6WQrLeSm7m7vk+C7qZaqacaeljMUVz7wCBxtrTi1fw/MMd4bnJ666hJqW5WY6kg0arG4M0S/OFqN4WkxGJ/l3yQ8HBiGwQx3W8vuZG5eBmb85idYt24dCgoKIIoiWJbFqFGjMHnyZAwZMgRsF8JgI4GB70jS8Uz+sSbeK9tZD7OBRbzF4FVpckJ2PP541Tjd47WSTrMmsmdqjudZJZE7Bo2kE6yGbzPyaHUIHU4URCKWJBkJVgMaWkfgoTnTEGedgtWFq7Hu2DqcbduOmOEsfvPNVkw2/RKAJxHyXEXphGUF6urqcO2116KkpAQ5OTl49913kZjo38D58ssvx86dOzFjxgx88MEH4VwyYhg5Fs12wS/0a86YdDAMg/QHHkDCVVehafMWtO3ahYZ330Xqz+/ulrF0Z1o1wzB4bslETBoYuLF2JNHG+V89KQsPa/RTgslqw4jpM/x+74nnltXElUhEpGjrnozOjAurbEZPY7VacdNNN0EURVRXV2Pfvn3Yu3cvCgsLkZCQgGuvvRaZmcEX3wuGl34yudMIEoO7J4AevpKOoZPNWIZhEGc2eG2SXjUpC2MDTNRkbA6XhJIapfbNv++YhguGeSZh1fuWZNz97z0Agjf4FiOHmhaHKgXpvS43LQaSDBwobwTPMrh+2iBkuDOG75pwF346/qf4x44v8Lcd76PJakRjeytuLNyC0eUc6sffA1GSu7UOPiGsK6xYsQKzZ8/Gww8/jBUrVmDFihV48skn/Y574IEH0NbWhpdffjmcy0UUI8/ik0LvhsP/vGWK101iys1VjbzsdIIxdk9II0louc2nw1akWDQxq1vOq4dFU2/l/GHJSIsNPqJEm8BDKpmmRMDD1ybrJEY4Cqqn4DgOGRkZyMjIwOzZs3H48GHs378fSd2wAr18bEanx/Dunrt6aA2+2cAFrKuvxTdBsCPj7CntIKGoWpG4clO9s+Vfv3kKfvD37ahrdap1boItgW5zR555PHz/ye+SEUruwNdFNWi2C36rdoZhMCR2FJzV87C7GphV8DRml+2GwHKovPJT3DH4Agy5onslQSBMDX/Dhg1YunQpAGDp0qVYv3697nGzZ89GbGz3xSd3Bb3NwOxES8CokO4y9oByM5z803z8bsHobrvGuUIr6YRaF55jGbAMMfiR8/AHJVnVypjx3ZyH0BPwPI9x48bhhhtugMnUMzH6Rh9JR9vyUZuQZTFwutnXl/mEs/oa/I5WZUT7drgknKxuQYyJ95ISAWBsVjyumzrQ63ckuqozrCYep+va8MbXyma53oSVZDMiJcaIE2db4BAk3Wg7sncxov40ZpftxtoRs/HQ4sfRcslcXFn0FQb+9FoU/2gx5CDbL3aFsDz8qqoqdfmYkZGBqqqqTl7Re9Az7Oc438qLviwzaNHqkF3JaCVFqEjMvO+D2xVInZJ9ZY2qhy/LcrensfcnfEsLLH5pB/aVNqBkxQKvGjsmA6trMKfkJGLrYY/9sPgUJ0vuYKOd3HPfFNWgusWBYak23e/Wt4b9gCANPgkT/vTIWQCBN3sHJVlxuFJpqagn0xo4BsPrS/HMtpVoNNrw3vCZcAhmbL78VnzEjMN7o9rANzeC6cZ9mE6fyMsuuwxnzvh3R/rjH//o9TPDMGE/QKtWrcKqVasAANXV1Z0cHR56GZzdnYXaH9BOpF0JfzRyLFyCjLL6diTZjBFrUjIo2Yp9ZY1Yv6ccf/7wCMAA47PiMXlwIs4bnIjzBiVGZHLprxg4b0lnX2mD+n9fSUdvdU3klWUXDwXg3avgvstGYF5eYFmJGODP3W0Rr5qkL2H63o/BRrBVNXnnAwSSlwYlWbF+bwUA/XwaA8fiVFwGNuWcj80509BmMAOSjDe+LgETm4qsuxcENZ5w6PRp2rp1a8C/paeno7KyEpmZmaisrERaWlpYg1m2bBmWLVsGAMjPzw/rXJ2hveky48346L6Lu62WeX9Cm3LelZBQ3t05qKy+DdmJkSlfDHg03cLKJiyePBAWA4d9ZQ144+sSvLztJNJiTfj2N7Op199FDBwbsLmHS5QxITseEwYmYEJ2Ao5XNfsdY+RZlKzwGDytNHjd1IEdfi++f8uxOvH+++9DkiQMGDAAOTk5SE9PR5LN83wvPT/4MsTNPjXsA3X+0mZABwrddHIG/GPCVQCUiYPsC5wrdSEs92nhwoVYvXo1Hn74YaxevRqLFi2K1Li6Ha/WbRxDjX2E0Hr4XQkzI/HM5Q3tEa1Lc89lIzBlSCJGpcchSbMv4BBEHCxvQm2Lgxr7MNCrpWNztqPit7/FfV/thTMnF3MGz4FYkQA+iM9Zu7ILJdt6xmALWg5sxRGGgclkwoEDBwAAeXl5SBrpiQy78fycoM/51OLxmPvsNvXnQB6+dqNWmz1P8FUV5uWl48MDinry+s3d6+ASwjL4Dz/8MK655hq89tprGDx4MN59910AQEFBAV566SW8+uqrAICLLroIR44cQUtLC7Kzs/Haa69h3rx54Y8+DLRLLlcnzYcpwaP18LtSDIo0g65tcSI1N7ISywXDUv1+Z+I5TB58bkJWoxnfjk+QZTz4/dtorD0BOXEQRuzdhopdHwMARlptWJA7F5uGXoirz8vCf3eXwzdJV7tpG0qNmQus1aitEXHX8uVISkpCY2MjTp06BZvNhrPwSLahVBwdkR6L84cmY8dJpUFLIA1f69Xra/jez0PegHh8eOAM5o/LwKxR3VeDSUtYBj85ORmffvqp3+/z8/NVYw8AX331VTiX6Ra0Ma+BwskooePl4XOhSzpGnoVDENFkdwUdNkfpeXxr6YyrPYmpVUeQ9tBDuKEsCz/IS8NvxpjQvmcPju3ch/IWRf4lTUh8m35rDX6wHn4sY0d1yRFMnTpVDU+Nj4/H+PFKX2fhjEdK6qy8gy/aXI6AHr6545pYvgZ/QIIZR5+4PKBE1B3029IK2pvI0UE5VUpoGML28BnUtzkhy8HHSVN6HiPHejUeWVi0HY1GKxKvWwKXIIEz8DCPGoXE665D0533YW/qcACe/rOi7GvwPQYzmAQpI8fgPL4MLMfhoosu0j0mydY1Dx/wdhAD5RFoPXw9DV/7umSbEeOzE2DiuYh1XguGfl1agWAPUAOEEjraz7Ur/WgNHIuaZiUGP9JVLSndh8EdXdXqEFDR0I5/jL8Sg5ur8F+TSal6qrkXtFo2MXaij4cfqkEebziDIajH+MkXICZGv0WpNrQz1P2lYCagzjx8ErY6NMWGz351aUjXjxT91+BrbkDfm43SdbQPUldyC3iOxZnGNgAyGk/sxlOfrUFsbCwyMzORnp4OWZYRGxuLceP066pQegalPLCEW/75HXYV1wGWeNRZ4uESZbhE76qn2v//cPwArNlVimlDvDOEQ+1ZkD9pIr7eJeFXM2cGPEZ7P4a6Qa8NEw0kwWi9er9WogCGpNgwc2Qqfjl3ZEjXjiT91uCf67LB/YVwyxnHmnjUtjoxkG1A+ZETyM3NBQAcO3YMe/fuBQAMGTKEGvxeBkmY21XsXV3WKUqQZO/7QrvpeUFuilc4JiE5xAzrB384CW2Xj++0n8RtM4bg40L/vKLOsJo69/C1kX56E4qRZ/HGLVNDvnYk6bcG/1yXDe4vhKtH5mXFYfuJGozkqmGNicV1110HjuMgyzJaW1vB8zyM3VjmgtI1AjVAaXOX/zXw/r1xO9qMTQhRzmNZJqjmQY/8YAwe+cGYkM4NKBU8CYGidEixtN5MvzX4108bhKLqVvw4PxvpvbBlXH9lfFYCOEjIZJuQOzIfnDvSh2GYgNospecxcIxutUxSCllbP4fIqZYOnK7eVmpEGxoayKkx8iw+++UlONvsOFfDCpl+a/CtRh5/vprKAr2N9DgTkplWcIyMYUNyeno4lCAxcKzuXhhpdandqCXRKh0Z/N6GV6JmB5PR0NQYDE3tvY4JFbIpvYoEqxFmxoV2mcewnODT3yk9S6C9G9Jf1qBjMDuLxEmJMfaavTbtOM5lGGWk6bcePqV3kmg14LSUhNOORDxJJZw+Q6AQXFXS0fzd4Q6D7mwf7euHZ/VoBVst2v2GvlyCgxp8Sq/Ck2zVdx+q/ohe9VkAeHnbSQDeEwL5ji8Z4V/qQksoJRW6m940lnCgBp/Sq+iswTWldxLoe9t2rNr9d8+EMDDJis9/dSkGJVnPydgiQVeyxnsj1OBTKJSw6Syr2lfjH5Ji687hRJzespcQLtTgUyLO1vsvhrELhdMIz1wzIeTEG0rPoo2z16MrZTZ6E1TSoVACkJsWXh37q8/LjtBIKOeKzjKsw83A7mmixcOPjndBoVB6lM4MeqAKk30Fc5Ro+NHxLigUSo/SmWST2Mf7RVNJh0KhUNwE8uCHpdrwz1umYmAfisjRg0o6FAqF4iaQpJMRb+7zxh7wePh9OOcKADX4FAolAgQy+NEihZBMW5uxb4sifXv0FAqlV6Cn4WfEmXHP7OE9MJrIYzawWD5zGBaMG9DTQwmLsDz8uro6zJkzB8OHD8ecOXNQX1/vd8zevXtx/vnnIy8vD+PHj8c777wTziUpFEovxDcOP8lmxM7fzMaEgQk9M6AIwzAMHpg3CmMGxPX0UMIiLIO/YsUKzJ49G8ePH8fs2bOxYsUKv2OsVivefPNNHDp0CFu2bMG9996LhoaGcC5LoVB6GVpJ55YLc7Dup+f34GgogQjL4G/YsAFLly4FACxduhTr16/3O2bEiBEYPlxZ1g0YMABpaWmorq4O57IUCqWXoW1wcv+cEb26Jnx/JiwNv6qqCpmZmQCAjIwMVFVVdXj8rl274HQ6MWzYMN2/r1q1CqtWrQIAOilQKH0IraTT1zc2o5lOv5nLLrsMZ874N/394x//6PUzwzAd1omurKzEjTfeiNWrV4MN0PV92bJlWLZsGQAgPz+/s6FRKJReglbS6W3tCSkeOjX4W7duDfi39PR0VFZWIjMzE5WVlUhLS9M9rqmpCQsWLMAf//hHTJ8+veujpVAovZK+XiunvxDWt7Rw4UKsXr0aALB69WosWrTI7xin04mrrroKN910ExYvXhzO5SgUSi+lr1fD7C+E9S09/PDD+OSTTzB8+HBs3boVDz/8MACgoKAAt99+OwDg3XffxbZt2/DPf/4TEydOxMSJE7F3796wB06hUHoPgTpeUXoXjCz3lq6R3uTn56OgoKCnh0GhUIJAlmUM+fWHAICSFQt6eDT9m45sJ12HUSiUsOnLjb37E9TgUygUSj+BGnwKhULpJ9AMCQqFEhGeuHIs8vp4rZlohxp8CoUSEX4yfXBPD4HSCVTSoVAolH4CNfgUCoXST6AGn0KhUPoJ1OBTKBRKP4EafAqFQuknUINPoVAo/QRq8CkUCqWfQA0+hUKh9BN6bbXMlJQU5OTkdPn11dXVSE1NjdyAziF07D0DHXvP0FfH3lvHXVJSgpqaGt2/9VqDHy59ubwyHXvPQMfeM/TVsffFcVNJh0KhUPoJ1OBTKBRKPyFqDf6yZct6eghdho69Z6Bj7xn66tj74rijVsOnUCgUijdR6+FTKBQKxRtq8CkUCqWfEHUGf8uWLRg5ciRyc3OxYsWKnh6OH7feeivS0tIwduxY9Xd1dXWYM2cOhg8fjjlz5qC+vh4AIMsyfvGLXyA3Nxfjx4/H7t27e2rYAIDS0lLMnDkTY8aMQV5eHp577jkAfWP8drsdU6dOxYQJE5CXl4ff//73AIDi4mJMmzYNubm5uPbaa+F0OgEADocD1157LXJzczFt2jSUlJT02NgJoihi0qRJ+MEPfgCg74w9JycH48aNw8SJE5Gfnw+gb9wzANDQ0IDFixdj1KhRGD16NHbs2NFnxq6LHEUIgiAPHTpULioqkh0Ohzx+/Hj50KFDPT0sL7788kv5+++/l/Py8tTfPfDAA/Kf//xnWZZl+c9//rP84IMPyrIsy5s2bZIvv/xyWZIkeceOHfLUqVN7ZMyEiooK+fvvv5dlWZabmprk4cOHy4cOHeoT45ckSW5ubpZlWZadTqc8depUeceOHfKPf/xjec2aNbIsy/Kdd94pv/jii7Isy/ILL7wg33nnnbIsy/KaNWvka665pmcGruHpp5+Wr7vuOnnBggWyLMt9ZuyDBw+Wq6urvX7XF+4ZWZblm266SX7llVdkWZZlh8Mh19fX95mx6xFVBv+bb76R586dq/78pz/9Sf7Tn/7UgyPSp7i42MvgjxgxQq6oqJBlWTGqI0aMkGVZlpctWyb/+9//1j2uN7Bw4UL5448/7nPjb21tlSdNmiTv3LlTTk5Oll0ulyzL3vfP3Llz5W+++UaWZVl2uVxycnKyLElSj425tLRUnjVrlvzpp5/KCxYskCVJ6jNj1zP4feGeaWhokHNycvw+u74w9kBElaRTXl6OgQMHqj9nZ2ejvLy8B0cUHFVVVcjMzAQAZGRkoKqqCkDvfj8lJSXYs2cPpk2b1mfGL4oiJk6ciLS0NMyZMwfDhg1DQkICeJ73G5927DzPIz4+HrW1tT029nvvvRdPPfUUWFZ5ZGtra/vM2BmGwdy5czF58mSsWrUKQN+454uLi5GamopbbrkFkyZNwu23347W1tY+MfZARJXBjwYYhgHDMD09jA5paWnBj370I/ztb39DXFyc19968/g5jsPevXtRVlaGXbt24ciRIz09pKD44IMPkJaWhsmTJ/f0ULrE9u3bsXv3bmzevBkvvPACtm3b5vX33nrPCIKA3bt346677sKePXtgs9n89gV769gDEVUGPysrC6WlperPZWVlyMrK6sERBUd6ejoqKysBAJWVlUhLSwPQO9+Py+XCj370I9xwww24+uqrAfSt8QNAQkICZs6ciR07dqChoQGCIADwHp927IIgoLGxEcnJyT0y3q+//hobN25ETk4OlixZgs8++wz33HNPnxg7GQ8ApKWl4aqrrsKuXbv6xD2TnZ2N7OxsTJs2DQCwePFi7N69u0+MPRBRZfCnTJmC48ePo7i4GE6nE2vXrsXChQt7elidsnDhQqxevRoAsHr1aixatEj9/ZtvvglZlrFz507Ex8erS8meQJZl3HbbbRg9ejTuv/9+9fd9YfzV1dVoaGgAALS3t+OTTz7B6NGjMXPmTKxbt0537OQ9rVu3DrNmzeoxT+7Pf/4zysrKUFJSgrVr12LWrFl4++23+8TYW1tb0dzcrP7/448/xtixY/vEPZORkYGBAwfi6NGjAIBPP/0UY8aM6RNjD0iP7iB0A5s2bZKHDx8uDx06VH7iiSd6ejh+LFmyRM7IyJB5npezsrLkV199Va6pqZFnzZol5+bmyrNnz5Zra2tlWVYiS372s5/JQ4cOlceOHSt/9913PTr2r776SgYgjxs3Tp4wYYI8YcIEedOmTX1i/Pv27ZMnTpwojxs3Ts7Ly5P/8Ic/yLIsy0VFRfKUKVPkYcOGyYsXL5btdrssy7Lc3t4uL168WB42bJg8ZcoUuaioqMfGruXzzz9Xo3T6wtiLiork8ePHy+PHj5fHjBmjPpN94Z6RZVnes2ePPHnyZHncuHHyokWL5Lq6uj4zdj1oaQUKhULpJ0SVpEOhUCiUwFCDT6FQKP0EavApFAqln0ANPoVCofQTqMGnUCiUfgI1+BQKhdJPoAafQqFQ+gn/DxwmGmEVkCv2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "plot_results_multiple(predictions_multiseq, y_test, configs['data']['sequence_length'])\n",
    "plot_results(predictions_fullseq, y_test)\n",
    "plot_results(predictions_pointbypoint, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deep",
   "language": "python",
   "name": "deep"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
